On Applying Q-Learning to Optimize Power Allocation in 2-users NOMA System

DOI: 10.14416/j.ind.tech.2023.04.004

Authors

  • Phetnakorn Aermsa-Ard Department of Electronic Engineering Technology, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok
  • Chonticha Wangsamad Department of Electronic Engineering Technology, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok
  • Kritsada Mamat Department of Electronic Engineering Technology, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok

Keywords:

NOMA, power allocation, Q-Learning

Abstract

This article considers a power domain non-orthogonal multiple access (NOMA) system which is a multiple access technique considered to be used in the 5G technology and beyond. Successive interference cancellation (SIC) is applied to decode user’s signals and power allocation significantly affects the system performance. In this article, we propose to apply Q-learning which is one of the machine learning methods to solve a transmit power allocation problem in a 2-users NOMA system where the objective function is to maximize the minimum transmission rate. We show how to transform NOMA system into O-Learning components namely agent, action, stage, reward, and environment which are very important for the learning process. Numerical results show that the Q-learning offers higher reward in each step. For the system performance, the bit rates of two users in the system are very close to each other when the Q-learning is applied. Furthermore, the Q-learning offers a higher minimum rate than that performed by dynamic power allocation methods in the literature and optimizers in Python’s library.

References

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Published

2023-04-13

Issue

Section

บทความวิจัย (Research article)